의약품 부작용 예측을 위한 빅데이터 분석 기술 동향

  • Published : 2017.09.30

Abstract

Keywords

References

  1. WG. McBride, Thalidomide and congential abnormalities. Lancet, Vol 2, pp. 1358, 1961.
  2. D. Choi, M. Choi, and A. Ko, Current status of pharmaceutial safety management in Korea, J. Korean Med. Assoc. Vol. 55, No. 9, pp. 827-834, 2012. https://doi.org/10.5124/jkma.2012.55.9.827
  3. 한국의약품안전관리원, http://www.drugsafe.or.kr/
  4. NK Choi, J. Lee and BJ Park, Recent international initiatives of drug safety management, J. Korean Med. Assoc. Vol. 55, No. 9, pp. 819-826, 2012. https://doi.org/10.5124/jkma.2012.55.9.819
  5. BJ Park, Application of big data for public health, J. Korean Med. Assoc. Vol. 57, No. 5, pp. 383-385, 2014. https://doi.org/10.5124/jkma.2014.57.5.383
  6. patientslikeme, http://www.patientslikeme.com/
  7. A. Bate and SJ Evans, Quantitative signal detection using spontaneous ADR reporting, Pharmacoepidemiol Drug Saf. Vol. 18, pp. 427-436, 2009. https://doi.org/10.1002/pds.1742
  8. T. Tamura, T. Sakaeda, K. Kadoyama, et al. Aspirin- and clopidogrel-associated bleeding complications: Data Mining of the public version of the FDA Adverse Event Reporting System, Int. J. Med. Sci, Vol. 9, pp. 441-446, 2012. https://doi.org/10.7150/ijms.4549
  9. K. Sarvnaz et al., Text and Data Mining Techniques in Adverse Drug Reaction Detection, ACM Computing Surveys, Vol. 47, No. 4, pp. 56:1-56:39, 2015.
  10. T. Sakaeda, A. Tamon, K. Kadoyama, and Y. Okuno, Data Mining of the Public Version of the FDA Adverse Event Reporting System, Int. J. of Med. Sci., Vol. 10, pp. 796-803.
  11. H. Kim and KY Rhew, Analysis of Adverse Drug Reaction Reports Using Text Mining, Korean J. Clin. Pharm, Vol. 27, No. 4, pp. 221-227, 2017. https://doi.org/10.24304/kjcp.2017.27.4.221
  12. H. Kim and KY Rhew, A Machine Learning Approach to Classification of Case Reports on Adverse Drug Reactions using Text Mining of Expert Opinions, Lecture Notes in Electronic Engineering, Vol. 474, pp. 1072-1077, 2018.
  13. KH Lim, et al, Comparison of WHO-ART Versus MedDRA, Internationally Standardized Terminology of Adverse Drug Reaction Classification, Korean. J. Cli. Pharm. Vol. 17, No. 1, pp. 46-52, 2007.
  14. CC Freifeld, JS Brownstein, CM. Menone, et al., Digital drug safety surveilance: monitoring pharmaceutical products in Twitter, Drug Saf., Vol. 37, No. 5, pp. 343-350, 2014. https://doi.org/10.1007/s40264-014-0155-x
  15. A. Sarker and G. Gonzalez, Portable automatic text classification for adverse drug reaction detection via multi-corpus training, Journal of Biomedical Informatics, Vol. 53, pp. 196-207, 2015. https://doi.org/10.1016/j.jbi.2014.11.002
  16. A. Cocos, A. G. Fiks, and A. J. Masino, Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts, J. of the American Medical Inoformatics Association, Vol. 24, No. 4, pp. 813-821, 2017. https://doi.org/10.1093/jamia/ocw180
  17. K. Lee, et al, Adverse Drug Event Detection in Tweets with Semi-Supervised Convolutional Neural Networks, In Proc. of International World Wide Web Conference, Perth, Australia, pp. 705-714, 2017.